





'''simple docstring'''






import os

import torch

from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version


if is_torch_version(""">=""", FSDP_PYTORCH_VERSION):
					import torch.distributed.checkpoint as dist_cp
					from torch.distributed.checkpoint.default_planner import DefaultLoadPlanner, DefaultSavePlanner
					from torch.distributed.checkpoint.optimizer import load_sharded_optimizer_state_dict
					from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
					from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType


lowerCAmelCase									=   get_logger(__name__)






def   __A    (				a_    :	Optional[Any]   ,a_    :	Optional[Any]   ,a_    :	Dict   ,a_    :	Tuple   ,a_    :	List[str]=0						):
		os.makedirs(a_   ,exist_ok=a_						)
		with FSDP.state_dict_type(
		    a_   ,fsdp_plugin.state_dict_type   ,fsdp_plugin.state_dict_config   ,fsdp_plugin.optim_state_dict_config						):
				lowerCAmelCase  :     Optional[Any]       						= model.state_dict()
				if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
						lowerCAmelCase  :     Optional[Any]       						= f'''{MODEL_NAME}.bin''' if model_index == 0 else f'''{MODEL_NAME}_{model_index}.bin'''
						lowerCAmelCase  :     Optional[int]       						= os.path.join(a_   ,a_						)
						if accelerator.process_index == 0:
								logger.info(f'''Saving model to {output_model_file}'''						)
								torch.save(a_   ,a_						)
								logger.info(f'''Model saved to {output_model_file}'''						)
				elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
						lowerCAmelCase  :     str       						= (
						    f'''{MODEL_NAME}_rank{accelerator.process_index}.bin'''
						    if model_index == 0
						    else f'''{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'''
						)
						lowerCAmelCase  :     List[str]       						= os.path.join(a_   ,a_						)
						logger.info(f'''Saving model to {output_model_file}'''						)
						torch.save(a_   ,a_						)
						logger.info(f'''Model saved to {output_model_file}'''						)
				elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
						lowerCAmelCase  :     List[str]       						= os.path.join(a_   ,f'''{MODEL_NAME}_{model_index}'''						)
						os.makedirs(a_   ,exist_ok=a_						)
						logger.info(f'''Saving model to {ckpt_dir}'''						)
						lowerCAmelCase  :     Optional[Any]       						= {"model": state_dict}

						dist_cp.save_state_dict(
						    state_dict=a_   ,storage_writer=dist_cp.FileSystemWriter(a_						)   ,planner=DefaultSavePlanner()   ,)
						logger.info(f'''Model saved to {ckpt_dir}'''						)






def   __A    (				a_    :	Any   ,a_    :	int   ,a_    :	Any   ,a_    :	Optional[int]   ,a_    :	str=0						):
		accelerator.wait_for_everyone()
		with FSDP.state_dict_type(
		    a_   ,fsdp_plugin.state_dict_type   ,fsdp_plugin.state_dict_config   ,fsdp_plugin.optim_state_dict_config						):
				if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
						if type(a_						) != FSDP and accelerator.process_index != 0:
								if not fsdp_plugin.sync_module_states:
										raise ValueError(
										    "Set the `sync_module_states` flag to `True` so that model states are synced across processes when "
										    "initializing FSDP object"						)
								return
						lowerCAmelCase  :     Any       						= f'''{MODEL_NAME}.bin''' if model_index == 0 else f'''{MODEL_NAME}_{model_index}.bin'''
						lowerCAmelCase  :     str       						= os.path.join(a_   ,a_						)
						logger.info(f'''Loading model from {input_model_file}'''						)
						lowerCAmelCase  :     Any       						= torch.load(a_						)
						logger.info(f'''Model loaded from {input_model_file}'''						)
				elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
						lowerCAmelCase  :     Union[str, Any]       						= (
						    f'''{MODEL_NAME}_rank{accelerator.process_index}.bin'''
						    if model_index == 0
						    else f'''{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'''
						)
						lowerCAmelCase  :     str       						= os.path.join(a_   ,a_						)
						logger.info(f'''Loading model from {input_model_file}'''						)
						lowerCAmelCase  :     Any       						= torch.load(a_						)
						logger.info(f'''Model loaded from {input_model_file}'''						)
				elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
						lowerCAmelCase  :     List[str]       						= (
						    os.path.join(a_   ,f'''{MODEL_NAME}_{model_index}'''						)
						    if f'''{MODEL_NAME}''' not in input_dir
						    else input_dir
						)
						logger.info(f'''Loading model from {ckpt_dir}'''						)
						lowerCAmelCase  :     Tuple       						= {"model": model.state_dict()}
						dist_cp.load_state_dict(
						    state_dict=a_   ,storage_reader=dist_cp.FileSystemReader(a_						)   ,planner=DefaultLoadPlanner()   ,)
						lowerCAmelCase  :     List[Any]       						= state_dict["model"]
						logger.info(f'''Model loaded from {ckpt_dir}'''						)
				model.load_state_dict(a_						)






def   __A    (				a_    :	str   ,a_    :	str   ,a_    :	Any   ,a_    :	Optional[int]   ,a_    :	Optional[int]   ,a_    :	Dict=0						):
		os.makedirs(a_   ,exist_ok=a_						)
		with FSDP.state_dict_type(
		    a_   ,fsdp_plugin.state_dict_type   ,fsdp_plugin.state_dict_config   ,fsdp_plugin.optim_state_dict_config						):
				lowerCAmelCase  :     Tuple       						= FSDP.optim_state_dict(a_   ,a_						)
				if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
						if accelerator.process_index == 0:
								lowerCAmelCase  :     List[str]       						= (
								    f'''{OPTIMIZER_NAME}.bin''' if optimizer_index == 0 else f'''{OPTIMIZER_NAME}_{optimizer_index}.bin'''
								)
								lowerCAmelCase  :     Optional[Any]       						= os.path.join(a_   ,a_						)
								logger.info(f'''Saving Optimizer state to {output_optimizer_file}'''						)
								torch.save(a_   ,a_						)
								logger.info(f'''Optimizer state saved in {output_optimizer_file}'''						)
				else:
						lowerCAmelCase  :     List[str]       						= os.path.join(a_   ,f'''{OPTIMIZER_NAME}_{optimizer_index}'''						)
						os.makedirs(a_   ,exist_ok=a_						)
						logger.info(f'''Saving Optimizer state to {ckpt_dir}'''						)
						dist_cp.save_state_dict(
						    state_dict={"optimizer": optim_state}   ,storage_writer=dist_cp.FileSystemWriter(a_						)   ,planner=DefaultSavePlanner()   ,)
						logger.info(f'''Optimizer state saved in {ckpt_dir}'''						)






def   __A    (				a_    :	List[Any]   ,a_    :	List[str]   ,a_    :	int   ,a_    :	Dict   ,a_    :	Optional[int]   ,a_    :	str=0						):
		accelerator.wait_for_everyone()
		with FSDP.state_dict_type(
		    a_   ,fsdp_plugin.state_dict_type   ,fsdp_plugin.state_dict_config   ,fsdp_plugin.optim_state_dict_config						):
				if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
						lowerCAmelCase  :     Optional[int]       						= None
						# below check should work but currently it isn't working (mostly opytorch issue),
						# in the meantime disabling it at the cost of excess memory usage
						# if accelerator.process_index == 0 or not fsdp_plugin.optim_state_dict_config.rank0_only:
						lowerCAmelCase  :     Optional[Any]       						= (
						    f'''{OPTIMIZER_NAME}.bin''' if optimizer_index == 0 else f'''{OPTIMIZER_NAME}_{optimizer_index}.bin'''
						)
						lowerCAmelCase  :     int       						= os.path.join(a_   ,a_						)
						logger.info(f'''Loading Optimizer state from {input_optimizer_file}'''						)
						lowerCAmelCase  :     Optional[int]       						= torch.load(a_						)
						logger.info(f'''Optimizer state loaded from {input_optimizer_file}'''						)
				else:
						lowerCAmelCase  :     Dict       						= (
						    os.path.join(a_   ,f'''{OPTIMIZER_NAME}_{optimizer_index}'''						)
						    if f'''{OPTIMIZER_NAME}''' not in input_dir
						    else input_dir
						)
						logger.info(f'''Loading Optimizer from {ckpt_dir}'''						)
						lowerCAmelCase  :     str       						= load_sharded_optimizer_state_dict(
						    model_state_dict=model.state_dict()   ,optimizer_key="optimizer"   ,storage_reader=dist_cp.FileSystemReader(a_						)   ,)
						lowerCAmelCase  :     Union[str, Any]       						= optim_state["optimizer"]
						logger.info(f'''Optimizer loaded from {ckpt_dir}'''						)
				lowerCAmelCase  :     str       						= FSDP.optim_state_dict_to_load(a_   ,a_   ,a_						)
				optimizer.load_state_dict(a_						)










'''simple docstring'''






from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst






def   __A    (				):
		lowerCAmelCase						,		lowerCAmelCase  :     List[Any]       						= 9, 1_4  # noqa: F841
		lowerCAmelCase  :     int       						= [
		    [0, 1, 4],
		    [0, 7, 8],
		    [1, 2, 8],
		    [7, 8, 7],
		    [7, 6, 1],
		    [2, 8, 2],
		    [8, 6, 6],
		    [2, 3, 7],
		    [2, 5, 4],
		    [6, 5, 2],
		    [3, 5, 1_4],
		    [3, 4, 9],
		    [5, 4, 1_0],
		    [1, 7, 1_1],
		]

		lowerCAmelCase  :     Optional[Any]       						= defaultdict(a_						)
		for nodea, nodea, cost in edges:
				adjancency[nodea].append([nodea, cost]						)
				adjancency[nodea].append([nodea, cost]						)

		lowerCAmelCase  :     int       						= mst(a_						)

		lowerCAmelCase  :     Tuple       						= [
		    [7, 6, 1],
		    [2, 8, 2],
		    [6, 5, 2],
		    [0, 1, 4],
		    [2, 5, 4],
		    [2, 3, 7],
		    [0, 7, 8],
		    [3, 4, 9],
		]

		for answer in expected:
				lowerCAmelCase  :     List[str]       						= tuple(answer[:2]						)
				lowerCAmelCase  :     int       						= tuple(edge[::-1]						)
				assert edge in result or reverse in result



